https://github.com/FarInHeight/To-Match-or-Not-to-Match
공식 코드To Match or Not to Match: Revisiting Image Matching for Reliable Visual Place Recognition
Papers with Code 아카이브
Retrieval
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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A methodology that involves selecting relevant data or examples from a large dataset to support tasks like prediction, learning, or inference. It enhances models by providing context or additional information, often used in systems like retrieval-augmented generation or in-context learning.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/FedericoAureliano/eudoxus
공식 코드https://github.com/FeiWang96/GTR
공식 코드Retrieving Complex Tables with Multi-Granular Graph Representation Learning
https://github.com/FilVa/PD-Rank
공식 코드https://github.com/FilipMiscevic/random_walk
공식 코드Simple Search Algorithms on Semantic Networks Learned from Language Use
https://github.com/FingerRec/OA-Transformer
공식 코드https://github.com/FudanDNN-NLP/RAG
공식 코드Searching for Best Practices in Retrieval-Augmented Generation
https://github.com/GRAAL-Research/quebec-insurance-rag-corpora
공식 코드Quebec Automobile Insurance Question-Answering With Retrieval-Augmented Generation
https://github.com/GanjinZero/RAMM
공식 코드RAMM: Retrieval-augmented Biomedical Visual Question Answering with Multi-modal Pre-training
https://github.com/GenjiB/ECLIPSE
공식 코드ECLIPSE: Efficient Long-range Video Retrieval using Sight and Sound
https://github.com/GeoTrouvetout/Vehicle_ReID
공식 코드https://github.com/GeondoPark/MHSAN
공식 코드MHSAN: Multi-Head Self-Attention Network for Visual Semantic Embedding
https://github.com/Georgetown-IR-Lab/LexBoost
공식 코드LexBoost: Improving Lexical Document Retrieval with Nearest Neighbors
https://github.com/Georgetown-IR-Lab/epic-neural-ir
공식 코드Expansion via Prediction of Importance with Contextualization
https://github.com/Georgetown-IR-Lab/multilingual-neural-ir
공식 코드Teaching a New Dog Old Tricks: Resurrecting Multilingual Retrieval Using Zero-shot Learning
https://github.com/Gompyn/re2com-opensource
공식 코드Retrieve and Refine: Exemplar-based Neural Comment Generation
https://github.com/GuangmingZhu/SketchIME
공식 코드Sketch Input Method Editor: A Comprehensive Dataset and Methodology for Systematic Input Recognition
https://github.com/Guzpenha/query_variation_generators
공식 코드Evaluating the Robustness of Retrieval Pipelines with Query Variation Generators
https://github.com/Guzpenha/transformer_rankers
공식 코드On the Calibration and Uncertainty of Neural Learning to Rank Models
https://github.com/Guzpenha/transformers_cl
공식 코드Curriculum Learning Strategies for IR: An Empirical Study on Conversation Response Ranking
https://github.com/H-shw/futurepedia
공식 코드Not All Languages are Equal: Insights into Multilingual Retrieval-Augmented Generation
https://github.com/HAWLYQ/ICECAP
공식 코드ICECAP: Information Concentrated Entity-aware Image Captioning
https://github.com/HCPLab-SYSU/Fine-Grained-Image-Captioning
공식 코드Fine-Grained Image Captioning with Global-Local Discriminative Objective
https://github.com/HHansi/Embed2Detect
공식 코드Embed2Detect: Temporally Clustered Embedded Words for Event Detection in Social Media
https://github.com/HITsz-TMG/KaLM-Embedding
공식 코드KaLM-Embedding-V2: Superior Training Techniques and Data Inspire A Versatile Embedding Model
https://github.com/HITsz-TMG/RaSeRec
공식 코드https://github.com/HITsz-TMG/SKURG
공식 코드https://github.com/HKUNLP/instructor-embedding
공식 코드One Embedder, Any Task: Instruction-Finetuned Text Embeddings
https://github.com/HLR/LvsR-LLM
공식 코드Learning vs Retrieval: The Role of In-Context Examples in Regression with LLMs
https://github.com/HMC-MIR/SheetMusicID
공식 코드https://github.com/HMC-MIR/piano_bootleg_scores
공식 코드https://github.com/HanNight/RE-T5
공식 코드https://github.com/Hang422/causal-thought-graph
공식 코드Causal Graphs Meet Thoughts: Enhancing Complex Reasoning in Graph-Augmented LLMs
https://github.com/HanjiangHu/DASGIL
공식 코드DASGIL: Domain Adaptation for Semantic and Geometric-aware Image-based Localization
https://github.com/HanjiangHu/DIFL-FCL
공식 코드Retrieval-based Localization Based on Domain-invariant Feature Learning under Changing Environments
https://github.com/HanjiangHu/DISAM
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.